基于投影的稀疏表示与非局部正则化图像复原方法

doi:10.3969/j.issn.0372-2112.2014.07.009

电子学报 ›› 2014, Vol. 42 ›› Issue (7): 1299-1304.DOI: 10.3969/j.issn.0372-2112.2014.07.009

基于投影的稀疏表示与非局部正则化图像复原方法

徐焕宇¹, 孙权森², 李大禹¹, 宣丽¹

1. 中国科学院长春光学精密机械与物理研究所应用光学国家重点实验室, 吉林长春 130033;
2. 南京理工大学计算机学院, 江苏南京 210094

收稿日期:2012-11-29 修回日期:2014-01-20 出版日期:2014-07-25
- 作者简介:
- 徐焕宇 男.1985年3月出生，吉林吉林人.2008年、2013年分别在南京理工大学获得工学学士、工学博士学位，现在中国科学院长春光学精密机械与物理研究所任助理研究员，研究方向为图像处理、自适应光学.E-mail：xhydtc@hotmail.com；孙权森 男.1963年11月出生，山东梁山人，教授、博士生导师.2006年于南京理工大学获得工学博士学位，现任职于南京理工大学计算机科学与技术学院，主要研究方向为模式识别与图像处理.E-mail：qssun@126.com
- 基金资助:
- 国家自然科学基金 (No.61273251，No.11174274，No.11174279，No.61205021，No.11204299，No.61377032，No.61378075）

Projection-Based Image Restoration via Sparse Representation and Nonlocal Regularization

XU Huan-yu¹, SUN Quan-sen², LI Da-yu¹, XUAN Li¹

1. State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin130033, China;
2. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China

Received:2012-11-29 Revised:2014-01-20 Online:2014-07-25 Published:2014-07-25
- Supported by:
- National Natural Science Foundation of China (No.61273251, No.11174274, No.11174279, No.61205021, No.11204299, No.61377032, No.61378075)

摘要/Abstract

摘要：

提出一种基于投影的稀疏表示与非局部正则化相结合的图像去模糊、去噪图像复原方法.该方法结合了自适应构造字典的稀疏表示与非局部总变差，提出的正则化模型分解为三个投影子问题进行求解以提高求解效率.实验结果表明，本文所提出的图像复原方法能够有效地保持原图像的纹理细节信息，对于不同程度的退化图像上均有较好的复原结果，在视觉效果和客观评价指标上均优于相比较的现有方法.

关键词: 图像复原, 稀疏表示, 非局部总变差, 正则化

Abstract:

This paper proposes a projection based sparse representation and nonlocal regularization deblurring and denoising image restoration algorithm.The algorithm combines sparse representation via adaptive learned dictionary and nonlocal total variation,and the proposed regularization model is divided into three projection sub problems to solve to improve the efficiency.Experimental results show that the proposed algorithm can preserve the detail information effectively,and have nice restoration results for images with different degree of degradation.The proposed algorithm achieves improvement on both visual appearance and objective indices compared with state-of-the-art methods.

Key words: image restoration, sparse representation, nonlocal total variation, regularization

中图分类号:

TP751.1

徐焕宇, 孙权森, 李大禹, 等. 基于投影的稀疏表示与非局部正则化图像复原方法[J]. 电子学报, 2014, 42(7): 1299-1304.

XU Huan-yu, SUN Quan-sen, LI Da-yu, et al. Projection-Based Image Restoration via Sparse Representation and Nonlocal Regularization[J]. Acta Electronica Sinica, 2014, 42(7): 1299-1304.

参考文献

[1] L Rudin,S Osher,E Fatemi.Nonlinear total variation based noise removal algorithms[J].Physica D:Nonlinear Phenomena,1992,60(1-4):259-268.
[2] Yiqiu Dong,Michael Hintermüller,M Monserrat Rincon-Camacho.Automated regularization parameter selection in multi-scale total variation models for image restoration[J].Journal of Mathematical Imaging and Vision,2011,40(1):82-104.
[3] 卢成武,宋国乡.带曲波域约束的全变差正则化抑噪方法[J].电子学报,2008,36(4):646-649. LU Cheng-wu,SONG Guo-xiang.Total variation regularization denoising method with constraint on curvelet-domain[J].Acta Electronica Sinica,2008,36(4):646-649.(in Chinese)
[4] M Bertalmio,V Caselles,B Rougé,et al.TV based image restoration with local constraints[J].Journal of Scientific Computing,2003,19(1-3):95-122.
[5] Buades A,Coll B,Morel J M.A non-local algorithm for image denoising[A].Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C].San Diego:IEEE,2005.Vol.2:60-65.
[6] 郑钰辉,孙权森,夏德深.基于2DPCA的有效非局部滤波方法[J].自动化学报,2010,36(10):1379-1389. ZHENG Yu-Hui,SUN Quan-Sen,XIA De-Shen.Anefficient 2DPCA-based non-local means filter[J].Acta Automatica Sinica,2010,36(10):1379-1389.(in Chinese)
[7] G Gilboa,S Osher.Nonlocal operators with applications to image processing[J].SIAM Multiscale Modeling and Simulation,2007,7(3):1005-1028.
[8] 孙玉宝,韦志辉,吴敏,等.稀疏性正则化的图像泊松去噪算法[J].电子学报,2011,39(2):285-290. SUN Yu-bao,WEI Zhi-hui,WU Min,et al.Image poisson denoising using sparse representations[J].Acta Electronica Sinica,2011,39(2):285-290.(in Chinese)
[9] Chao Jia,Brian L Evans.Patch-based image deconvolution via joint modeling of sparse priors[A].Proceedings of IEEE International Conference on Image Processing[C].Brussels:IEEE,2011.681-684.
[10] M Aharon,et al.K-SVD:an algorithm for designing of overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322.
[11] J Mairal,et al.Non-local sparse models for image restoration[A].Proceedings of IEEE International Conference on Computer Vision[C].Kyoto:IEEE,2009.2272-2279.
[12] Weisheng Dong,et al.Sparsity-based image denoising via dictionary learning and structural clustering[A].Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition[C].Colorado Springs:IEEE,2011.457-464.
[13] M Elad,M Aharon.Image denoising via sparse and redundant representations over learned dictionaries[J].IEEE Transactions on Image Processing,2006,15(12):3736-3745.
[14] P Chatterjee,P Milanfar.Clustering-based denoising with locally learned dictionaries[J].IEEE Transactions on Image Processing,2009,18(7):1438-1451.
[15] L Zhang,W Dong,D Zhang,et al.Two-stage image denoising by principal component analysis with local pixel grouping[J].Pattern Recognition,2010,43(4):1531-1549.
[16] G Gilboa,S Osher.Nonlocal operators with applications to image processing[J].SIAM Multiscale Modeling and Simulation,2007,7(3):1005-1028.
[17] X Zhang,M Burger,X Bresson,et al.Bregmanized nonlocal regularization for deconvolution and sparse reconstruction[J].SIAM Journal on Imaging Sciences,2010,3(3):253-276.
[18] Xin Li.Fine-granularity and spatially-adaptive regularization for projection-based image deblurring[J].IEEE Transactions on Image Processing,2011,20(4):971-983.
[19] T Goldstein,S Osher.The split Bregman method for L1 regularized problems[J].SIAM Journal on Imaging Sciences,2009,2(2):323-343.
[20] I Daubechies,M Defriese,C DeMol.An iterative thresholding algorithm for linear inverse problems with a sparsity constraint[J].Communications on Pure and Applied Mathematics,2004,57(11):1413-1457.

基于投影的稀疏表示与非局部正则化图像复原方法

Projection-Based Image Restoration via Sparse Representation and Nonlocal Regularization

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	韩亮, 蔡文涛, 蒲秀娟, 罗统军, 黄谦. 使用信噪比正则化LightGBM的腹部源胎儿心电信号提取方法[J]. 电子学报, 2022, 50(8): 1793-1800.
[2]	江兵兵, 何文达, 吴兴宇, 项俊浩, 洪立斌, 盛伟国. 基于自适应图学习的半监督特征选择[J]. 电子学报, 2022, 50(7): 1643-1652.
[3]	杨晓城, 杨真乙, 阎敬业, 武林, 蒋明峰. 基于拉普拉斯算子的综合孔径辐射计成像算法[J]. 电子学报, 2022, 50(2): 339-345.
[4]	刘浩阳, 林耀进, 刘景华, 吴镒潾, 毛煜, 李绍滋. 由粗到细的分层特征选择[J]. 电子学报, 2022, 50(11): 2778-2789.
[5]	冯西安, 寇思玮, 谭伟杰, 毕杨. 水声信号处理中的稀疏表示理论及应用[J]. 电子学报, 2021, 49(9): 1840-1851.
[6]	禤韵怡, 杨春玲. 基于帧间组稀疏的两阶段递归增强视频压缩感知重构网络[J]. 电子学报, 2021, 49(3): 435-442.
[7]	夏楠, 高紫俊, 李博, 王珏. 一种基于循环稀疏表示的同频带干扰抑制及发射源定位方法[J]. 电子学报, 2021, 49(1): 8-13.
[8]	仇祝令, 查宇飞, 吴敏, 王青. 基于注意力学习的正则化相关滤波跟踪算法[J]. 电子学报, 2020, 48(9): 1762-1768.
[9]	李东瑾, 杨瑞娟, 李晓柏, 董睿杰. 基于栈式稀疏降噪自编码网络的辐射源调制识别[J]. 电子学报, 2020, 48(6): 1198-1204.
[10]	郑学炜, 杨春玲, 禤韵怡. CVS中基于视频运动特征的多假设-双稀疏重构算法[J]. 电子学报, 2020, 48(2): 249-257.
[11]	脱婷, 马慧芳, 李志欣, 赵卫中. 熵权约束稀疏表示的短文本分类算法[J]. 电子学报, 2020, 48(11): 2131-2137.
[12]	吉利鹏, 倪锦根. 偏差补偿符号子带自适应滤波器[J]. 电子学报, 2020, 48(11): 2220-2225.
[13]	蒲磊, 冯新喜, 侯志强, 查宇飞, 余旺盛. 基于深度空间正则化的相关滤波跟踪算法[J]. 电子学报, 2020, 48(10): 2025-2032.
[14]	周浦城, 张杰, 薛模根, 尹璋堃. 基于卷积分析稀疏表示和相位一致性的低照度图像增强[J]. 电子学报, 2020, 48(1): 180-188.
[15]	任开旭, 王玉龙, 刘同存, 李炜. 融合多维语义表示的概率矩阵分解模型[J]. 电子学报, 2019, 47(9): 1848-1854.